Adaptive event-triggered control for stochastic nonholonomic systems with unknown virtual control coefficients

Abstract This paper presents an event-triggered adaptive neural network control algorithm for stochastic nonholonomic systems with virtual control coefficients being unknown time-varying functions under a novel event-triggered threshold mechanism. The event-triggered mechanism can switch smoothly between fixed and relative threshold, which is firstly considered for stochastic nonholonomic systems to reduce the communication burden. We give a state-input scaling transformation to transform the stochastic nonholonomic systems into a new system to make it easier to design the controller. By introducing a monotone-decreasing positive continuous function, we construct a suitable adaptive event-triggered controller that can overcome the effect of unknown virtual control coefficients on system performance, while ensuring that all states of the closed-loop system are bounded in probability. An adaptive event-triggered control-based switching strategy is employed to eliminate the uncontrollability phenomenon. The effectiveness of the established control approach is demonstrated through an example.